"""
BSD 3-Clause License

Copyright (c) Soumith Chintala 2016,
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

* Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


Copyright 2020 Huawei Technologies Co., Ltd

Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

https://spdx.org/licenses/BSD-3-Clause.html

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import torch
import torch.nn as nn
import numpy as np
from functools import partial
import torch.nn.init as init
import torch.nn.functional as F
import math
from timm.models.layers import DropPath, to_2tuple

DROPOUT_FLOPS = 4
LAYER_NORM_FLOPS = 5
ACTIVATION_FLOPS = 8
SOFTMAX_FLOPS = 5

class GroupLinear(nn.Module):
    '''
    Group Linear operator 
    '''
    def __init__(self, in_planes, out_channels,groups=1, bias=True):
        super(GroupLinear, self).__init__()
        assert in_planes%groups==0
        assert out_channels%groups==0
        self.in_dim = in_planes
        self.out_dim = out_channels
        self.groups=groups
        self.bias = bias
        self.group_in_dim = int(self.in_dim/self.groups)
        self.group_out_dim = int(self.out_dim/self.groups)

        self.group_weight = nn.Parameter(torch.zeros(self.groups, self.group_in_dim, self.group_out_dim))
        self.group_bias=nn.Parameter(torch.zeros(self.out_dim))

    def forward(self, x):
        t,b,d=x.size()
        x = x.view(t,b,self.groups,int(d/self.groups))
        out = torch.einsum('tbgd,gdf->tbgf', (x, self.group_weight)).reshape(t,b,self.out_dim)+self.group_bias
        return out
    def extra_repr(self):
        s = ('{in_dim}, {out_dim}')
        if self.groups != 1:
            s += ', groups={groups}'
        if self.bias is None:
            s += ', bias=False'
        return s.format(**self.__dict__)


class Mlp(nn.Module):
    '''
    MLP with support to use group linear operator
    '''
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0., group=1):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        if group==1:
            self.fc1 = nn.Linear(in_features, hidden_features)
            self.fc2 = nn.Linear(hidden_features, out_features)
        else:
            self.fc1 = GroupLinear(in_features, hidden_features,group)
            self.fc2 = GroupLinear(hidden_features, out_features,group)
        self.act = act_layer()

        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x

class GroupNorm(nn.Module):
    def __init__(self, num_groups, embed_dim, eps=1e-5, affine=True):
        super().__init__()
        self.gn = nn.GroupNorm(num_groups, embed_dim,eps,affine)

    def forward(self, x):
        B,T,C = x.shape
        x = x.view(B*T,C)
        x = self.gn(x)
        x = x.view(B,T,C)
        return x


class Attention(nn.Module):
    '''
    Multi-head self-attention
    from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    with some modification to support different num_heads and head_dim.
    '''
    def __init__(self, dim, num_heads=8, head_dim=None, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        if head_dim is not None:
            self.head_dim=head_dim
        else:
            head_dim = dim // num_heads
            self.head_dim = head_dim
        self.scale = qk_scale or head_dim ** -0.5

        self.qkv = nn.Linear(dim, self.head_dim* self.num_heads * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(self.head_dim* self.num_heads, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x, padding_mask=None):
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        # B,heads,N,C/heads 
        q, k, v = qkv[0], qkv[1], qkv[2]
        
        # trick here to make q@k.t more stable
        attn = ((q * self.scale) @ k.transpose(-2, -1))
        if padding_mask is not None:
            attn = attn.view(B, self.num_heads, N, N)
            attn = attn.masked_fill(
                padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
                float("-inf"),
            )
            attn_float = attn.softmax(dim=-1, dtype=torch.float32)
            attn = attn_float.type_as(attn)
        else:
            attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x = (attn @ v).transpose(1, 2).reshape(B, N, self.head_dim* self.num_heads)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x
        
class Block(nn.Module):
    '''
    Pre-layernorm transformer block
    '''
    def __init__(self, dim, num_heads, head_dim=None, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, group=1, skip_lam=1.):
        super().__init__()
        self.dim = dim
        self.mlp_hidden_dim = int(dim * mlp_ratio)
        self.skip_lam = skip_lam

        self.norm1 = norm_layer(dim)
        self.attn = Attention(
            dim, num_heads=num_heads, head_dim=head_dim, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        self.mlp = Mlp(in_features=dim, hidden_features=self.mlp_hidden_dim, act_layer=act_layer, drop=drop, group=group)

    def forward(self, x, padding_mask=None):
        x = x + self.drop_path(self.attn(self.norm1(x),padding_mask))/self.skip_lam
        x = x + self.drop_path(self.mlp(self.norm2(x)))/self.skip_lam
        return x

    def flops(self, s):
        heads = self.attn.num_heads
        h = self.dim
        i = self.mlp_hidden_dim
        mha_block_flops = dict(
        kqv=3 * h * h  ,
        attention_scores=h * s,
        attn_softmax=SOFTMAX_FLOPS * s * heads,
        attention_dropout=DROPOUT_FLOPS * s * heads,
        attention_scale=s * heads,
        attention_weighted_avg_values=h * s,
        attn_output=h * h,
        attn_output_bias=h,
        attn_output_dropout=DROPOUT_FLOPS * h,
        attn_output_residual=h,
        attn_output_layer_norm=LAYER_NORM_FLOPS * h,)
        ffn_block_flops = dict(
        intermediate=h * i,
        intermediate_act=ACTIVATION_FLOPS * i,
        intermediate_bias=i,
        output=h * i,
        output_bias=h,
        output_dropout=DROPOUT_FLOPS * h,
        output_residual=h,
        output_layer_norm=LAYER_NORM_FLOPS * h,)

        return sum(mha_block_flops.values())*s + sum(ffn_block_flops.values())*s

class MHABlock(nn.Module):
    """
    Multihead Attention block with residual branch
    """
    def __init__(self, dim, num_heads, head_dim=None, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, group=1, skip_lam=1.):
        super().__init__()
        self.dim = dim
        self.norm1 = norm_layer(dim)
        self.skip_lam = skip_lam
        self.attn = Attention(
            dim, num_heads=num_heads, head_dim=head_dim, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x, padding_mask=None):
        x = x + self.drop_path(self.attn(self.norm1(x*self.skip_lam), padding_mask))/self.skip_lam
        return x

    def flops(self, s):
        heads = self.attn.num_heads
        h = self.dim
        block_flops = dict(
        kqv=3 * h * h ,
        attention_scores=h * s,
        attn_softmax=SOFTMAX_FLOPS * s * heads,
        attention_dropout=DROPOUT_FLOPS * s * heads,
        attention_scale=s * heads,
        attention_weighted_avg_values=h * s,
        attn_output=h * h,
        attn_output_bias=h,
        attn_output_dropout=DROPOUT_FLOPS * h,
        attn_output_residual=h,
        attn_output_layer_norm=LAYER_NORM_FLOPS * h,)

        return sum(block_flops.values())*s

class FFNBlock(nn.Module):
    """
    Feed forward network with residual branch
    """
    def __init__(self, dim, num_heads, head_dim=None, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, group=1, skip_lam=1.):
        super().__init__()
        self.skip_lam = skip_lam
        self.dim = dim
        self.mlp_hidden_dim = int(dim * mlp_ratio)

        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        self.mlp = Mlp(in_features=dim, hidden_features=self.mlp_hidden_dim, act_layer=act_layer, drop=drop, group=group)
    def forward(self, x):
        x = x + self.drop_path(self.mlp(self.norm2(x*self.skip_lam)))/self.skip_lam
        return x
    def flops(self, s):
        heads = self.attn.num_heads
        h = self.dim
        i = self.mlp_hidden_dim
        block_flops = dict(
        intermediate=h * i,
        intermediate_act=ACTIVATION_FLOPS * i,
        intermediate_bias=i,
        output=h * i,
        output_bias=h,
        output_dropout=DROPOUT_FLOPS * h,
        output_residual=h,
        output_layer_norm=LAYER_NORM_FLOPS * h,)

        return sum(block_flops.values())*s

class HybridEmbed(nn.Module):
    """ CNN Feature Map Embedding
    Extract feature map from CNN, flatten, project to embedding dim.
    from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    """
    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
        super().__init__()
        assert isinstance(backbone, nn.Module)
        img_size = to_2tuple(img_size)
        self.img_size = img_size
        self.backbone = backbone
        if feature_size is None:
            with torch.no_grad():
                training = backbone.training
                if training:
                    backbone.eval()
                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
                feature_size = o.shape[-2:]
                feature_dim = o.shape[1]
                backbone.train(training)
        else:
            feature_size = to_2tuple(feature_size)
            feature_dim = self.backbone.feature_info.channels()[-1]
        self.num_patches = feature_size[0] * feature_size[1]
        self.proj = nn.Conv2d(feature_dim, embed_dim,kernel_size=1)

    def forward(self, x):
        x = self.backbone(x)[-1]
        x = self.proj(x)
        return x


class PatchEmbedNaive(nn.Module):
    """ 
    Image to Patch Embedding
    from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.embed_dim = embed_dim

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        B, C, H, W = x.shape
        assert H == self.img_size[0] and W == self.img_size[1], \
            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        x = self.proj(x)
        return x

    def flops(self):
        img_size = self.img_size[0]
        block_flops = dict(
        proj=img_size*img_size*3*self.embed_dim,
        )
        return sum(block_flops.values())


class PatchEmbed4_2(nn.Module):
    """ 
    Image to Patch Embedding with 4 layer convolution
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()

        new_patch_size = to_2tuple(patch_size // 2)

        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.embed_dim = embed_dim

        self.conv1 = nn.Conv2d(in_chans, 64, kernel_size=7, stride=2, padding=3, bias=False)  # 112x112
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)  # 112x112
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)  
        self.bn3 = nn.BatchNorm2d(64)

        self.proj = nn.Conv2d(64, embed_dim, kernel_size=new_patch_size, stride=new_patch_size)
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)

        x = self.proj(x)  # [B, C, W, H]

        return x

    def flops(self):
        img_size = self.img_size[0]
        block_flops = dict(
        conv1=img_size/2*img_size/2*3*64*7*7,
        conv2=img_size/2*img_size/2*64*64*3*3,
        conv3=img_size/2*img_size/2*64*64*3*3,
        proj=img_size/2*img_size/2*64*self.embed_dim,
        )
        return sum(block_flops.values())

    
class PatchEmbed4_2_128(nn.Module):
    """ 
    Image to Patch Embedding with 4 layer convolution and 128 filters
    """
    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
        super().__init__()

        new_patch_size = to_2tuple(patch_size // 2)

        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches
        self.embed_dim = embed_dim

        self.conv1 = nn.Conv2d(in_chans, 128, kernel_size=7, stride=2, padding=3, bias=False)  # 112x112
        self.bn1 = nn.BatchNorm2d(128)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False)  # 112x112
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False)  
        self.bn3 = nn.BatchNorm2d(128)

        self.proj = nn.Conv2d(128, embed_dim, kernel_size=new_patch_size, stride=new_patch_size)
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu(x)

        x = self.proj(x)  # [B, C, W, H]

        return x
    def flops(self):
        img_size = self.img_size[0]
        block_flops = dict(
        conv1=img_size/2*img_size/2*3*128*7*7,
        conv2=img_size/2*img_size/2*128*128*3*3,
        conv3=img_size/2*img_size/2*128*128*3*3,
        proj=img_size/2*img_size/2*128*self.embed_dim,
        )
        return sum(block_flops.values())